When teaching and discussing statistical assumptions, our focus is oftentimes placed on how to test and address potential violations rather than the effects of violating assumptions on the estimates produced by our statistical models. The latter represents a potential avenue to help us better understand the impact of researcher degrees of freedom on the statistical estimates we produce. The Violating Assumptions Series is an endeavor I have undertaken to demonstrate the effects of violating assumptions on the estimates produced across various statistical models. The series will review assumptions associated with estimating causal associations, as well as more complicated statistical models including, but not limited to, multilevel models, path models, structural equation models, and Bayesian models. In addition to the primary goal, the series of posts is designed to illustrate how simulations can be used to develop a comprehensive understanding of applied statistics.

翻译：在教学和讨论统计假设时,我们的重点往往放在如何测试和处理可能的违规情况,而不是违反对统计模型估计的假设的影响,统计模型是帮助我们更好地了解研究者自由度对我们所编制的统计估计的影响的潜在途径。《违反假设系列》是我为证明违反对各种统计模型产生的估计的假设所产生的影响而做的一项努力。系列将审查与估计因果关联有关的假设,以及更为复杂的统计模型,包括但不限于多级模型、路径模型、结构等式模型和巴伊西亚模型。除了主要目标外,系列员额的设计还旨在说明如何利用模拟来全面了解应用的统计数据。